
"""simple docstring"""



import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import DecisionTransformerModel
 from transformers.models.decision_transformer.modeling_decision_transformer import (
     DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )




class 		a_    :







    def __init__(      self      :							str  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Optional[Any]=13  ,       __UpperCamelCase      :							Any=7  ,       __UpperCamelCase      :							List[Any]=6  ,       __UpperCamelCase      :							int=17  ,       __UpperCamelCase      :							Union[str, Any]=23  ,       __UpperCamelCase      :							Union[str, Any]=11  ,       __UpperCamelCase      :							Tuple=True  ,       )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = seq_length
        _UpperCAmelCase        = act_dim
        _UpperCAmelCase        = state_dim
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = is_training







    def   _snake_case       (      self      :							int  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = floats_tensor((self.batch_size, self.seq_length, self.state_dim)  )
        _UpperCAmelCase        = floats_tensor((self.batch_size, self.seq_length, self.act_dim)  )
        _UpperCAmelCase        = floats_tensor((self.batch_size, self.seq_length, 1)  )
        _UpperCAmelCase        = floats_tensor((self.batch_size, self.seq_length, 1)  )
        _UpperCAmelCase        = ids_tensor((self.batch_size, self.seq_length)  ,       vocab_size=10_00  )
        _UpperCAmelCase        = random_attention_mask((self.batch_size, self.seq_length)  )

        _UpperCAmelCase        = self.get_config()

        return (
            config,
            states,
            actions,
            rewards,
            returns_to_go,
            timesteps,
            attention_mask,
        )







    def   _snake_case       (      self      :							List[str]  )		->Dict:






        '''simple docstring'''






        return DecisionTransformerConfig(
            batch_size=self.batch_size  ,       seq_length=self.seq_length  ,       act_dim=self.act_dim  ,       state_dim=self.state_dim  ,       hidden_size=self.hidden_size  ,       max_length=self.max_length  ,       )







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							List[Any]  ,       )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = DecisionTransformerModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )

        self.parent.assertEqual(result.state_preds.shape  ,       states.shape  )
        self.parent.assertEqual(result.action_preds.shape  ,       actions.shape  )
        self.parent.assertEqual(result.return_preds.shape  ,       returns_to_go.shape  )
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length * 3, self.hidden_size)  )  # seq length *3 as there are 3 modelities: states, returns and actions







    def   _snake_case       (      self      :							Optional[int]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,
        )        = config_and_inputs
        _UpperCAmelCase        = {
            """states""": states,
            """actions""": actions,
            """rewards""": rewards,
            """returns_to_go""": returns_to_go,
            """timesteps""": timesteps,
            """attention_mask""": attention_mask,
        }
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					str			     =					(DecisionTransformerModel,) if is_torch_available() else ()
    a   :					Any			     =					()
    a   :					Optional[int]			     =					{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

    # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
    a   :					str			     =					False

    # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
    a   :					str			     =					False
    a   :					Union[str, Any]			     =					False
    a   :					Optional[int]			     =					False
    a   :					Union[str, Any]			     =					False
    a   :					Dict			     =					False
    a   :					List[Any]			     =					False
    a   :					str			     =					False
    a   :					str			     =					False
    a   :					str			     =					False







    def   _snake_case       (      self      :							Dict  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = DecisionTransformerModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							List[str]  )		->int:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def   _snake_case       (      self      :							List[Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    @slow
    def   _snake_case       (      self      :							Any  )		->Optional[int]:






        '''simple docstring'''






        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _UpperCAmelCase        = DecisionTransformerModel.from_pretrained(__UpperCamelCase  )
            self.assertIsNotNone(__UpperCamelCase  )







    def   _snake_case       (      self      :							str  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            _UpperCAmelCase        = inspect.signature(model.forward  )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _UpperCAmelCase        = [*signature.parameters.keys()]

            _UpperCAmelCase        = [
                """states""",
                """actions""",
                """rewards""",
                """returns_to_go""",
                """timesteps""",
                """attention_mask""",
            ]

            self.assertListEqual(arg_names[: len(__UpperCamelCase  )]  ,       __UpperCamelCase  )







@require_torch
class 		a_    (			unittest.TestCase						):







    @slow
    def   _snake_case       (      self      :							str  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = 2  # number of steps of autoregressive prediction we will perform
        _UpperCAmelCase        = 10  # defined by the RL environment, may be normalized
        _UpperCAmelCase        = DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""  )
        _UpperCAmelCase        = model.to(__UpperCamelCase  )
        _UpperCAmelCase        = model.config
        torch.manual_seed(0  )
        _UpperCAmelCase        = torch.randn(1  ,       1  ,       config.state_dim  ).to(device=__UpperCamelCase  ,       dtype=torch.floataa  )  # env.reset()

        _UpperCAmelCase        = torch.tensor(
            [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]  ,       device=__UpperCamelCase  )

        _UpperCAmelCase        = torch.tensor(__UpperCamelCase  ,       device=__UpperCamelCase  ,       dtype=torch.floataa  ).reshape(1  ,       1  ,       1  )
        _UpperCAmelCase        = state
        _UpperCAmelCase        = torch.zeros(1  ,       0  ,       config.act_dim  ,       device=__UpperCamelCase  ,       dtype=torch.floataa  )
        _UpperCAmelCase        = torch.zeros(1  ,       0  ,       device=__UpperCamelCase  ,       dtype=torch.floataa  )
        _UpperCAmelCase        = torch.tensor(0  ,       device=__UpperCamelCase  ,       dtype=torch.long  ).reshape(1  ,       1  )

        for step in range(__UpperCamelCase  ):
            _UpperCAmelCase        = torch.cat([actions, torch.zeros(1  ,       1  ,       config.act_dim  ,       device=__UpperCamelCase  )]  ,       dim=1  )
            _UpperCAmelCase        = torch.cat([rewards, torch.zeros(1  ,       1  ,       device=__UpperCamelCase  )]  ,       dim=1  )

            _UpperCAmelCase        = torch.ones(1  ,       states.shape[1]  ).to(dtype=torch.long  ,       device=states.device  )

            with torch.no_grad():
                _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model(
                    states=__UpperCamelCase  ,       actions=__UpperCamelCase  ,       rewards=__UpperCamelCase  ,       returns_to_go=__UpperCamelCase  ,       timesteps=__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       return_dict=__UpperCamelCase  ,       )

            self.assertEqual(action_pred.shape  ,       actions.shape  )
            self.assertTrue(torch.allclose(action_pred[0, -1]  ,       expected_outputs[step]  ,       atol=1e-4  )  )
            _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = (  # env.step(action)
                torch.randn(1  ,       1  ,       config.state_dim  ).to(device=__UpperCamelCase  ,       dtype=torch.floataa  ),
                1.0,
                False,
                {},
            )

            _UpperCAmelCase        = action_pred[0, -1]
            _UpperCAmelCase        = torch.cat([states, state]  ,       dim=1  )
            _UpperCAmelCase        = returns_to_go[0, -1] - reward
            _UpperCAmelCase        = torch.cat([returns_to_go, pred_return.reshape(1  ,       1  ,       1  )]  ,       dim=1  )
            _UpperCAmelCase        = torch.cat(
                [timesteps, torch.ones((1, 1)  ,       device=__UpperCamelCase  ,       dtype=torch.long  ) * (step + 1)]  ,       dim=1  )

"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Dict       		=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a       :						Dict       		=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase        = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""   )
            _UpperCAmelCase        = value
        else:
            _UpperCAmelCase        = value

    return new_state_dict


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = image.size
    _UpperCAmelCase        = max(_A       ,   _A   )
    _UpperCAmelCase        = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
    _UpperCAmelCase        = target_max_size / current_max_size
    _UpperCAmelCase        = image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = F.to_tensor(_A   )
    _UpperCAmelCase        = F.normalize(_A       ,   mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   )
    return image


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    logger.info("""Converting model..."""   )

    # load original state dict
    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_A       ,   _A       ,   _A   )
    _UpperCAmelCase        = rename_backbone_keys(_A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            _UpperCAmelCase        = state_dict.pop(_A   )
            _UpperCAmelCase        = val
    # create HuggingFace model and load state dict
    _UpperCAmelCase        = TableTransformerConfig(
        backbone="""resnet18"""       ,   mask_loss_coefficient=1       ,   dice_loss_coefficient=1       ,   ce_loss_coefficient=1       ,   bbox_loss_coefficient=5       ,   giou_loss_coefficient=2       ,   eos_coefficient=0.4       ,   class_cost=1       ,   bbox_cost=5       ,   giou_cost=2       ,   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = 1_5
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = {0: """table""", 1: """table rotated"""}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase        = 1_2_5
        _UpperCAmelCase        = 6
        _UpperCAmelCase        = {
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    _UpperCAmelCase        = DetrImageProcessor(
        format="""coco_detection"""       ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
    _UpperCAmelCase        = TableTransformerForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion
    _UpperCAmelCase        = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
    _UpperCAmelCase        = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=_A   )
    _UpperCAmelCase        = Image.open(_A   ).convert("""RGB"""   )
    _UpperCAmelCase        = normalize(resize(_A       ,   _A   )   ).unsqueeze(0   )

    _UpperCAmelCase        = model(_A   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = (1, 1_5, 3)
        _UpperCAmelCase        = torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

    else:
        _UpperCAmelCase        = (1, 1_2_5, 7)
        _UpperCAmelCase        = torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        image_processor.save_pretrained(_A   )

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""   )
        _UpperCAmelCase        = (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_A   )
        image_processor.push_to_hub(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)